scholarly journals Recovery of Weak Factor Loadings When Adding the Mean Structure in Confirmatory Factor Analysis: A Simulation Study

2016 ◽  
Vol 6 ◽  
Author(s):  
Carmen Ximénez
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Simulation Study ◽  
Factor Loadings ◽  
Weak Factor ◽  
Confirmatory Factor ◽  
Mean Structure ◽  
The Mean

scholarly journals Can Variances of Latent Variables be Scaled in Such a Way That They Correspond to Eigenvalues?

2017 ◽  
Vol 6 (6) ◽  
pp. 35 ◽  
Author(s):  
Karl Schweizer ◽  
Stefan Troche ◽  
Siegbert Reiß
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Simulation Study ◽  
Latent Variables ◽  
Latent Variable ◽  
Factor Model ◽  
Estimation Method ◽  
Factor Loadings ◽  
Confirmatory Factor ◽  
Theoretical Foundations

The paper reports an investigation of whether sums of squared factor loadings obtained in confirmatory factor analysis correspond to eigenvalues of exploratory factor analysis. The sum of squared factor loadings reflects the variance of the corresponding latent variable if the variance parameter of the confirmatory factor model is set equal to one. Hence, the computation of the sum implies a specific type of scaling of the variance. While the investigation of the theoretical foundations suggested the expected correspondence between sums of squared factor loadings and eigenvalues, the necessity of procedural specifications in the application, as for example the estimation method, revealed external influences on the outcome. A simulation study was conducted that demonstrated the possibility of exact correspondence if the same estimation method was applied. However, in the majority of realized specifications the estimates showed similar sizes but no correspondence. 

A Comparison of Reliability Estimation Based on Confirmatory Factor Analysis and Exploratory Structural Equation Models

2021 ◽  
pp. 001316442110089
Author(s):  
Yuanshu Fu ◽  
Zhonglin Wen ◽  
Yang Wang
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Structural Equation ◽  
Reliability Estimation ◽  
Model Fit ◽  
Equation Modeling ◽  
Factor Loadings ◽  
Reliability Estimates ◽  
Confirmatory Factor ◽  
Composite Reliability

Composite reliability, or coefficient omega, can be estimated using structural equation modeling. Composite reliability is usually estimated under the basic independent clusters model of confirmatory factor analysis (ICM-CFA). However, due to the existence of cross-loadings, the model fit of the exploratory structural equation model (ESEM) is often found to be substantially better than that of ICM-CFA. The present study first illustrated the method used to estimate composite reliability under ESEM and then compared the difference between ESEM and ICM-CFA in terms of composite reliability estimation under various indicators per factor, target factor loadings, cross-loadings, and sample sizes. The results showed no apparent difference in using ESEM or ICM-CFA for estimating composite reliability, and the rotation type did not affect the composite reliability estimates generated by ESEM. An empirical example was given as further proof of the results of the simulation studies. Based on the present study, we suggest that if the model fit of ESEM (regardless of the utilized rotation criteria) is acceptable but that of ICM-CFA is not, the composite reliability estimates based on the above two models should be similar. If the target factor loadings are relatively small, researchers should increase the number of indicators per factor or increase the sample size.

Internet Lifestyle of Iranian Users

2015 ◽  
Vol 6 (1) ◽  
pp. 1-15 ◽  
Author(s):  
Amir Abedini Koshksaray ◽  
Tayebeh Farahani
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Construct Validity ◽  
T Test ◽  
The Internet ◽  
High Effectiveness ◽  
Internet Users ◽  
Confirmatory Factor ◽  
Perceived Importance ◽  
The Mean

One distinguishing factor of individuals is their lifestyles. In the internet, every individual shows a different behavior while some groups have rather similar behaviors. By identifying these groups and their interests and preferences, it becomes possible to offer a product or advertising which is compatible with their wants. This leads to conveying the value presented by the producer to the consumer with high effectiveness. One source of identifying target customers or internet users is their lifestyle in internet space. The present study aims at describing and explaining internet lifestyle (e-lifestyle) of Iranian users. In particular, this study tries to find out the most common e-lifestyle of Iranian users. To this aim, 412 students involved with internet were surveyed. After estimating the construct validity of the study through confirmatory factor analysis, the mean scores of each e-lifestyle were compared by student t-test. The results revealed that Iranian users have mostly entertainment-driven e-lifestyles. The perceived importance-driven, sociability-driven, interest-driven, novelty-driven, need-driven, and uninterested or concern-driven e-lifestyles were respectively identified after that. This is the first study investigating the most common e-lifestyle among Internet users.

The Empirical Verification of an Assignment of Items to Subtests

2008 ◽  
Vol 68 (6) ◽  
pp. 923-939 ◽  
Author(s):  
Ilse Stuive ◽  
Henk A. L. Kiers ◽  
Marieke E. Timmerman ◽  
Jos M. F. ten Berge
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Simulation Study ◽  
Common Factor ◽  
Model Error ◽  
Unique Variance ◽  
Empirical Verification ◽  
Multiple Group ◽  
Analysis Methods ◽  
Confirmatory Factor

This study compares two confirmatory factor analysis methods on their ability to verify whether correct assignments of items to subtests are supported by the data. The confirmatory common factor (CCF) method is used most often and defines nonzero loadings so that they correspond to the assignment of items to subtests. Another method is the oblique multiple group (OMG) method, which defines subtests as unweighted sums of the scores on all items assigned to the subtest, and (corrected) correlations are used to verify the assignment. A simulation study compares both methods, accounting for the influence of model error and the amount of unique variance. The CCF and OMG methods show similar behavior with relatively small amounts of unique variance and low interfactor correlations. However, at high amounts of unique variance and high interfactor correlations, the CCF detected correct assignments more often, whereas the OMG was better at detecting incorrect assignments.

Effect of Variable and Subject Sampling on Recovery of Weak Factors In CFA

Methodology ◽  
2007 ◽  
Vol 3 (2) ◽  
pp. 67-80 ◽  
Author(s):  
Carmen Ximénez
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Sample Size ◽  
Exploratory Factor Analysis ◽  
Estimation Method ◽  
Simulation Studies ◽  
Weak Factor ◽  
Variable Sampling ◽  
Improper Solutions ◽  
Confirmatory Factor

Abstract. Two general issues central to the design of a study are subject sampling and variable sampling. Previous research has examined their effects on factor pattern recovery in the context of exploratory factor analysis. The present paper focuses on recovery of weak factors and reports two simulation studies in the context of confirmatory factor analysis. Conditions investigated include the estimation method (ML vs. ULS), sample size (100, 300, and 500), number of variables per factor (3, 4, or 5), loading size in the weak factor (.25 or .35), and factor correlation (null vs. moderate). Results show that both subject and variable sample size affect the recovery of weak factors, particularly if factors are not correlated. A small but consistent pattern of differences between methods occurs, which favors the use of ULS. Additionally, the frequency of nonconvergent and improper solutions is also affected by the same variables.

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